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Bioinformatics pipeline summary


Where we see the pipeline processes

Author: Adrien Taudière

Date: November 26, 2024

Code
library(knitr)
library(targets)
library(MiscMetabar)
library(patchwork)
here::i_am("analysis/01_bioinformatics.qmd")
source(here::here("R/styles.R"))
source(here::here("R/functions.R"))

Carbon footprint

Code
res_ga <- greenAlgoR::ga_targets()
No local metadata. Did you remember to run tar_meta_download()?
Details: https://books.ropensci.org/targets/cloud-storage.html.
(Or maybe you need to run the pipeline with tar_make()?)
Silence this message with Sys.setenv(TAR_WARN = "false").
Code
library("emojifont")
ggvalue_box(
  round(res_ga$carbon_footprint_total_gCO2), 
  "Carbon footprint (g)", 
  icons = fontawesome(search_fontawesome("industry")[1]), 
  alpha_icon = 0.7,
  colors = idest_pal$ligth_color_idest[[1]][[3]],
  color_icon = idest_pal$dark_color_idest[[1]][[5]], 
  value_font_color = idest_pal$dark_color_idest[[1]][[5]], 
  lab_font_color = idest_pal$dark_color_idest[[1]][[5]]) +
ggvalue_box(
  round(res_ga$energy_needed_kWh,2), 
  "Energy used (kWh)", 
  icons = fontawesome(search_fontawesome("bolt")), 
  alpha_icon = 0.7,
  colors = idest_pal$ligth_color_idest[[1]][[1]],
  color_icon = idest_pal$dark_color_idest[[1]][[2]], 
  value_font_color = idest_pal$dark_color_idest[[1]][[2]], 
  lab_font_color = idest_pal$dark_color_idest[[1]][[2]]) 

Summary of the bioinformatic pipeline

Timeline and cpu usage

Code
log_data <- autometric::log_read(
  here::here("data/data_final/autometric_log.txt"),
  units_time = "hours",
  units_memory = "gigabytes"
)

log_data |>
  filter(!grepl("conclude:", phase)) |>
  filter(!grepl("prepare:", phase)) |>
  filter(!grepl("__DEFAULT__", phase)) |>
  ggplot(aes(x = time, y = reorder(phase, desc(time)), color = resident)) +
  geom_line(aes(linewidth = cpu)) +
  scale_color_viridis_b("Memory (Gb)", end = 0.9, direction = -1) +
  theme_idest() +
  xlab("Time (in hours)") +
  ylab("Targets")
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Code
d_pq <- clean_pq(tar_read("d_vs", store=here::here("_targets/")))
Cleaning suppress 3 taxa and 13 samples.
Code
summary_plot_pq(d_pq)
Cleaning suppress 0 taxa and 0 samples.

Code
tar_glimpse(script=here::here("_targets.R"), targets_only = TRUE, callr_arguments = list(show = FALSE))
Code
tar_meta(store=here::here("_targets/"), targets_only = TRUE) |> 
  dplyr::mutate(time = paste0(seconds %/% 3600,":",seconds %/% 60,":",floor(seconds %% 60)))|>
  dplyr::select(name, seconds, bytes, format, time) |>
  dplyr::mutate(Gb=round(bytes/10^9,2)) |>
  dplyr::arrange(desc(seconds), desc(bytes))  |> 
  kable()
name seconds bytes format time Gb
tax_tab 9016.268 1409221 rds 2:150:16 0.00
d_asv 7343.974 1900092 rds 2:122:23 0.00
ddR 1214.368 119739753 qs 0:20:14 0.12
ddF 1097.653 106072172 qs 0:18:17 0.11
quality_raw_seq 528.616 18320 rds 0:8:48 0.00
filtered 528.211 182 rds 0:8:48 0.00
quality_seq_wo_primers 474.257 18660 rds 0:7:54 0.00
cutadapt 366.159 86016 file 0:6:6 0.00
quality_seq_filtered_trimmed_FW 234.158 11403 rds 0:3:54 0.00
quality_seq_filtered_trimmed_REV 231.438 11403 rds 0:3:51 0.00
derep_rs 172.165 1966750496 qs 0:2:52 1.97
derep_fs 160.487 1133735591 qs 0:2:40 1.13
err_rs 118.445 20670 qs 0:1:58 0.00
track_by_samples 117.299 9196 rds 0:1:57 0.00
merged_seq 103.936 2361929 qs 0:1:43 0.00
err_fs 97.837 22269 qs 0:1:37 0.00
bioinfo_report 57.951 44 rds 0:0:57 0.00
seqtab_wo_chimera 28.870 1399147 rds 0:0:28 0.00
d_vs 8.722 1521944 rds 0:0:8 0.00
d_vs_mumu 5.887 1486389 rds 0:0:5 0.00
seq_tab_Pairs 0.605 1738865 rds 0:0:0 0.00
d_vs_mumu_rarefy 0.303 1422773 rds 0:0:0 0.00
data_phyloseq 0.191 1756245 rds 0:0:0 0.00
s_d 0.057 10856 rds 0:0:0 0.00
data_raw 0.038 5385 rds 0:0:0 0.00
track_sequences_samples_clusters 0.030 374 rds 0:0:0 0.00
seqtab 0.019 1383720 rds 0:0:0 0.00
asv_tab 0.016 1383563 rds 0:0:0 0.00
sam_tab 0.003 3487 rds 0:0:0 0.00
fastq_files_folder 0.001 49152 file 0:0:0 0.00
file_sam_data_csv 0.001 4376 file 0:0:0 0.00
file_refseq_taxo 0.000 114270891 file 0:0:0 0.11
data_fnfs 0.000 2792 rds 0:0:0 0.00
data_fnrs 0.000 2791 rds 0:0:0 0.00
samp_n_otu_table 0.000 1855 rds 0:0:0 0.00

Load phyloseq object from targets store

Code
d_pq <- tar_read("d_vs", store=here::here("_targets/"))

The {targets} package is at the core of this project. Please read the intro of the user manual if you don’t know {targets}.

The {targets} package store … targets in a folder and can load (tar_load()) and read (tar_read) object from this folder.

Sample data

Code
DT::datatable(d_pq@sam_data)

Sequences, samples and clusters across the pipeline

Session Information

Session information are detailed below. More information about the machine, the system, as well as python and R packages, are available in the file data_final/information_run.txt .

Code
sessionInfo()
R version 4.4.2 (2024-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Debian GNU/Linux 12 (bookworm)

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0

locale:
 [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=fr_FR.UTF-8        LC_COLLATE=fr_FR.UTF-8    
 [5] LC_MONETARY=fr_FR.UTF-8    LC_MESSAGES=fr_FR.UTF-8   
 [7] LC_PAPER=fr_FR.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C       

time zone: Europe/Paris
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] emojifont_0.5.5    patchwork_1.3.0    MiscMetabar_0.10.1 purrr_1.0.2       
 [5] dplyr_1.1.4        dada2_1.34.0       Rcpp_1.0.13-1      ggplot2_3.5.1     
 [9] phyloseq_1.50.0    targets_1.9.0      knitr_1.49        

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3          jsonlite_1.8.9             
  [3] magrittr_2.0.3              farver_2.1.2               
  [5] rmarkdown_2.29              zlibbioc_1.52.0            
  [7] vctrs_0.6.5                 multtest_2.62.0            
  [9] Rsamtools_2.22.0            benchmarkme_1.0.8          
 [11] htmltools_0.5.8.1           S4Arrays_1.6.0             
 [13] Rhdf5lib_1.28.0             SparseArray_1.6.0          
 [15] rhdf5_2.50.0                sass_0.4.9                 
 [17] bslib_0.8.0                 htmlwidgets_1.6.4          
 [19] plyr_1.8.9                  cachem_1.1.0               
 [21] GenomicAlignments_1.42.0    igraph_2.1.1               
 [23] lifecycle_1.0.4             iterators_1.0.14           
 [25] pkgconfig_2.0.3             Matrix_1.7-1               
 [27] R6_2.5.1                    fastmap_1.2.0              
 [29] GenomeInfoDbData_1.2.13     MatrixGenerics_1.18.0      
 [31] digest_0.6.37               showtext_0.9-7             
 [33] colorspace_2.1-1            ShortRead_1.64.0           
 [35] S4Vectors_0.44.0            ps_1.8.1                   
 [37] rprojroot_2.0.4             crosstalk_1.2.1            
 [39] GenomicRanges_1.58.0        base64url_1.4              
 [41] hwriter_1.3.2.1             vegan_2.6-8                
 [43] labeling_0.4.3              fansi_1.0.6                
 [45] httr_1.4.7                  abind_1.4-8                
 [47] mgcv_1.9-1                  compiler_4.4.2             
 [49] here_1.0.1                  withr_3.0.2                
 [51] doParallel_1.0.17           backports_1.5.0            
 [53] BiocParallel_1.40.0         MASS_7.3-61                
 [55] DelayedArray_0.32.0         biomformat_1.34.0          
 [57] permute_0.9-7               tools_4.4.2                
 [59] ape_5.8                     glue_1.8.0                 
 [61] callr_3.7.6                 nlme_3.1-166               
 [63] rhdf5filters_1.18.0         grid_4.4.2                 
 [65] cluster_2.1.6               reshape2_1.4.4             
 [67] ade4_1.7-22                 generics_0.1.3             
 [69] gtable_0.3.6                data.table_1.16.2          
 [71] utf8_1.2.4                  XVector_0.46.0             
 [73] BiocGenerics_0.52.0         foreach_1.5.2              
 [75] pillar_1.9.0                stringr_1.5.1              
 [77] benchmarkmeData_1.0.4       splines_4.4.2              
 [79] lattice_0.22-6              showtextdb_3.0             
 [81] renv_1.0.11                 survival_3.7-0             
 [83] deldir_2.0-4                tidyselect_1.2.1           
 [85] Biostrings_2.74.0           IRanges_2.40.0             
 [87] SummarizedExperiment_1.36.0 stats4_4.4.2               
 [89] xfun_0.49                   Biobase_2.66.0             
 [91] matrixStats_1.4.1           DT_0.33                    
 [93] proto_1.0.0                 visNetwork_2.1.2           
 [95] stringi_1.8.4               UCSC.utils_1.2.0           
 [97] yaml_2.3.10                 evaluate_1.0.1             
 [99] codetools_0.2-20            interp_1.1-6               
[101] tibble_3.2.1                autometric_0.1.2           
[103] BiocManager_1.30.25         cli_3.6.3                  
[105] RcppParallel_5.1.9          secretbase_1.0.3           
[107] jquerylib_0.1.4             munsell_0.5.1              
[109] processx_3.8.4              GenomeInfoDb_1.42.0        
[111] greenAlgoR_0.1.1            png_0.1-8                  
[113] parallel_4.4.2              latticeExtra_0.6-30        
[115] jpeg_0.1-10                 bitops_1.0-9               
[117] pwalign_1.2.0               viridisLite_0.4.2          
[119] scales_1.3.0                sysfonts_0.8.9             
[121] crayon_1.5.3                rlang_1.1.4                

Citation

BibTeX citation:
@online{taudière2024,
  author = {Taudière, Adrien},
  title = {Bioinformatics Pipeline Summary},
  date = {2024-11-26},
  langid = {en}
}
For attribution, please cite this work as:
Taudière, Adrien. 2024. “Bioinformatics Pipeline Summary.” November 26, 2024.